Chinese Writer Identification Based on the Distribution of Character Skeleton

In this paper, a kind of Chinese character writer identification method is proposed and tested. Firstly, considering handwriting can be texture image in some sense, the Gabor wavelet is used to extract texture feature. Then a local direction contribution method (LDCM) is adopted to extract the local features of feature characters. In practice, we first skeletonise the character and then compute the skeleton direction distribution in each sub-region. Nearest neighbor classifier based on weighted Euclidean distance is utilized in classification. Experiment results verifies that the classification performance of LDCM is better than the Gabor method, and the correct identification rate of Top-3 candidates can reach 100% under the random combination of 3 feature characters.